{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kidney Exchange QAOA Example\n",
    "Used samples from Classiq github on QAOA and internet for pyomo models  \n",
    "Modified as needed for specfic problem and limitations based on account. \n",
    "\n",
    "<u>What is the Problem?</u>  \n",
    "Currently there are more than 100,000 patients on the waitling list in the United States for a kidney transplant from a deceased donor. This is addressed by the a program called the Kidney Exchange Program.  This program won the Nobel Prize in Economics for Alvin E. Roth and Lloyd S. Shapley's contributions to the <i>theory of stable matchings and the design of markets on 2012.</i>\n",
    "In summary, in a donor pair there is a recipient who needs a kidney transplant and a donor who is willing to give their kidney to the recipient. About $\\frac{1}{3}$ of those pairs are not compatible for a direct exchange. This is tackled by considering two incompatible pairs together: donor 1 may be compatible with recpient 2 and donor 2 may be compatible with recpient 1. In this example a two-way swap becomes feasible. This is the core of the kideny exchange program.  \n",
    "\n",
    "This is consdered an NP-Hard combinatorial optimization problem that becomes exponetially more difficult as the size of the pool increases. The longest chain in history involved 35 tranplants in the United States in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import classiq\n",
    "from classiq import *\n",
    "\n",
    "from pyomo.environ import *\n",
    "from itertools import product\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx  # noqa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import io\n",
    "import sys\n",
    "import time\n",
    "from contextlib import redirect_stdout\n",
    "from typing import List, Tuple, cast  # noqa\n",
    "\n",
    "import pyomo.environ as pyo\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the pyomo model for a simple kidney exhange problem \n",
    "\n",
    "In this very simple example, patients and donors represent sets of patients that receive a kidney from a donor. Compatibility is a dictionary mapping of patient-donor paris to their compatibilty scores. Binary decision variables are defined for each patient-donor pair x[donor,patient]. The objective is to maximize the total compatibility score. $ Maximize \\sum_{d,p\\in A}^{} \\sum_{m\\in M}c_{dp}x_{dpm}$ where d=donors, p=patients and c=compatability score. The contraints are added to ensure that each donor donates only once $\\sum_{d,p\\in A}^{}x_{dpm} = y_{dm}$ and each patient receives once $\\sum_{d,p\\in A}^{}x_{dpm} = y_{pm}$. We are creating a PYOMO model that gets fed into Classiq, as illustrated in Classiq documentation. We also solve initially with a classical solver to get inital results which can be compared to the QAOA results in the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mOptimal solution:\u001b[0m\n",
      "donor1 donates kidney to patient1\n",
      "donor2 donates kidney to patient3\n",
      "donor3 donates kidney to patient2\n",
      "\n",
      "\u001b[1m\u001b[4mModel Details\u001b[0m\n",
      "5 Set Declarations\n",
      "    donor_constraint_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {1, 2, 3}\n",
      "    patient_constraint_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {1, 2, 3}\n",
      "    x_index : Size=1, Index=None, Ordered=True\n",
      "        Key  : Dimen : Domain              : Size : Members\n",
      "        None :     2 : x_index_0*x_index_1 :    9 : {('donor1', 'patient1'), ('donor1', 'patient2'), ('donor1', 'patient3'), ('donor2', 'patient1'), ('donor2', 'patient2'), ('donor2', 'patient3'), ('donor3', 'patient1'), ('donor3', 'patient2'), ('donor3', 'patient3')}\n",
      "    x_index_0 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {'donor1', 'donor2', 'donor3'}\n",
      "    x_index_1 : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    3 : {'patient1', 'patient2', 'patient3'}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=9, Index=x_index\n",
      "        Key                    : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "        ('donor1', 'patient1') :     0 :   1.0 :     1 : False : False : Binary\n",
      "        ('donor1', 'patient2') :     0 :   0.0 :     1 : False : False : Binary\n",
      "        ('donor1', 'patient3') :     0 :   0.0 :     1 : False : False : Binary\n",
      "        ('donor2', 'patient1') :     0 :   0.0 :     1 : False : False : Binary\n",
      "        ('donor2', 'patient2') :     0 :   0.0 :     1 : False : False : Binary\n",
      "        ('donor2', 'patient3') :     0 :   1.0 :     1 : False : False : Binary\n",
      "        ('donor3', 'patient1') :     0 :   0.0 :     1 : False : False : Binary\n",
      "        ('donor3', 'patient2') :     0 :   1.0 :     1 : False : False : Binary\n",
      "        ('donor3', 'patient3') :     0 :   0.0 :     1 : False : False : Binary\n",
      "\n",
      "1 Objective Declarations\n",
      "    obj : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : maximize : 0.9*x[donor1,patient1] + 0.7*x[donor1,patient2] + 0.6*x[donor1,patient3] + 0.8*x[donor2,patient1] + 0.75*x[donor2,patient2] + 0.65*x[donor2,patient3] + 0.85*x[donor3,patient1] + 0.8*x[donor3,patient2] + 0.7*x[donor3,patient3]\n",
      "\n",
      "2 Constraint Declarations\n",
      "    donor_constraint : Size=3, Index=donor_constraint_index, Active=True\n",
      "        Key : Lower : Body                                                         : Upper : Active\n",
      "          1 :  -Inf : x[donor1,patient1] + x[donor1,patient2] + x[donor1,patient3] :   1.0 :   True\n",
      "          2 :  -Inf : x[donor2,patient1] + x[donor2,patient2] + x[donor2,patient3] :   1.0 :   True\n",
      "          3 :  -Inf : x[donor3,patient1] + x[donor3,patient2] + x[donor3,patient3] :   1.0 :   True\n",
      "    patient_constraint : Size=3, Index=patient_constraint_index, Active=True\n",
      "        Key : Lower : Body                                                         : Upper : Active\n",
      "          1 :  -Inf : x[donor1,patient1] + x[donor2,patient1] + x[donor3,patient1] :   1.0 :   True\n",
      "          2 :  -Inf : x[donor1,patient2] + x[donor2,patient2] + x[donor3,patient2] :   1.0 :   True\n",
      "          3 :  -Inf : x[donor1,patient3] + x[donor2,patient3] + x[donor3,patient3] :   1.0 :   True\n",
      "\n",
      "9 Declarations: x_index_0 x_index_1 x_index x obj donor_constraint_index donor_constraint patient_constraint_index patient_constraint\n"
     ]
    }
   ],
   "source": [
    "from pyomo.environ import *\n",
    "\n",
    "# Sample data: patient-donor pairs and compatibility scores\n",
    "donors = ['donor1', 'donor2', 'donor3']\n",
    "patients = ['patient1', 'patient2', 'patient3']\n",
    "N=len(patients)\n",
    "M=len(donors)\n",
    "# Parameters\n",
    "compatibility_scores = {\n",
    "    ('donor1', 'patient1'): 0.9,\n",
    "    ('donor1', 'patient2'): 0.7,\n",
    "    ('donor1', 'patient3'): 0.6,\n",
    "    ('donor2', 'patient1'): 0.8,\n",
    "    ('donor2', 'patient2'): 0.75,\n",
    "    ('donor2', 'patient3'): 0.65,\n",
    "    ('donor3', 'patient1'): 0.85,\n",
    "    ('donor3', 'patient2'): 0.8,\n",
    "    ('donor3', 'patient3'): 0.7,\n",
    "}\n",
    "\n",
    "# Create Pyomo model\n",
    "model = ConcreteModel()\n",
    "\n",
    "# Variables\n",
    "model.x = Var(donors, patients, within=Binary)\n",
    "\n",
    "# Objective\n",
    "model.obj = Objective(expr=sum(compatibility_scores[donor, patient] * model.x[donor, patient]\n",
    "                                for donor in donors\n",
    "                                for patient in patients),\n",
    "                      sense=maximize)\n",
    "\n",
    "# Constraints\n",
    "model.donor_constraint = ConstraintList()\n",
    "for donor in donors:\n",
    "    model.donor_constraint.add(sum(model.x[donor, patient] for patient in patients) <= 1)\n",
    "\n",
    "model.patient_constraint = ConstraintList()\n",
    "for patient in patients:\n",
    "    model.patient_constraint.add(sum(model.x[donor, patient] for donor in donors) <= 1)\n",
    "\n",
    "\n",
    "\n",
    "# Solve\n",
    "solver = SolverFactory('glpk')\n",
    "solver.solve(model)\n",
    "\n",
    "# Output\n",
    "print(\"\\033[1m\\033[4mOptimal solution:\\033[0m\")\n",
    "for donor in donors:\n",
    "    for patient in patients:\n",
    "        if model.x[donor, patient].value == 1:\n",
    "            print(f\"{donor} donates kidney to {patient}\")\n",
    "\n",
    "print(\"\\n\\033[1m\\033[4mModel Details\\033[0m\")\n",
    "model.pprint()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start Generatng the QAOA Process\n",
    "  \n",
    "### Create the inital parameters for the quantum circuit. These can me modified as needed.\n",
    "1. Defining the number of layers (num_layers) of the QAOA Ansatz.  \n",
    "2. Define the penalty_energy for invalid solutions, which influences the convergence rate. Smaller positive values are preferred, but shoudl be tweaked.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq import (\n",
    "    Preferences,\n",
    "    construct_combinatorial_optimization_model,\n",
    "    set_preferences,\n",
    ")\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=5, penalty_energy=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the classical optimizer part of the QAOA. These parameters can be modified.\n",
    "1. opt_type is the classical optimizer type. Choices include, COBYLA, SPSA, ADAM, L_BFGS_B, and NELDER_MEAD\n",
    "2. The max_iterations is the maximum number of optimzer iterations and is set to 100.  \n",
    "3. The alpha_cvar is a parameter that describes the quantile considered in the CVAR expectation value. See https://arxiv.org/abs/1907.04769 for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(\n",
    "    #opt_type='COBYLA',\n",
    "    max_iteration=200,\n",
    "    alpha_cvar=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine everthing together to form the entire QAOA model as a QMOD.\n",
    "1. PYOMO Model  \n",
    "2. QAOA quantum circuit  \n",
    "3. Clasical optimizer  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")\n",
    "\n",
    "# defining cosntraint such as computer and parameters for a quicker and more optimized circuit.\n",
    "preferences = Preferences(transpilation_option=\"none\", timeout_seconds=3000)\n",
    "\n",
    "qmod = set_preferences(qmod, preferences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Wite out the QMOD and preferences to a JSON file  \n",
    "2. Synthesize the model in Classiq interface  \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "write_qmod(qmod, \"Kidney Exchange\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Sythesize the quantum model\n",
    "2. Show the quantm model in the Classiq platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq import show, synthesize\n",
    "qmod = set_constraints(qmod,Constraints(optimization_parameter='width'))\n",
    "qprog = synthesize(qmod)\n",
    "#show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the quantum model and store the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the convergence graph  \n",
    "<i>Important to remember that this is a maximization problem when looking at the graph</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result=res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve and Display the Solutions\n",
    "- Print them out\n",
    "- Graph using a histogram\n",
    "- Show Donor - Recipients in Network Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print out the top 10 solutions with the highest cost or objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m\u001b[4mTop 10 Solutions\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.012695</td>\n",
       "      <td>2.35</td>\n",
       "      <td>[1, 0, 0, 0, 0, 1, 0, 1, 0]</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.008789</td>\n",
       "      <td>2.35</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 1]</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.005859</td>\n",
       "      <td>2.20</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 0, 0, 1]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.010254</td>\n",
       "      <td>2.20</td>\n",
       "      <td>[0, 0, 1, 1, 0, 0, 0, 1, 0]</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.010742</td>\n",
       "      <td>2.20</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 1, 0, 0]</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.009277</td>\n",
       "      <td>2.20</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 1, 0, 0]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.003906</td>\n",
       "      <td>1.70</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 1, 0]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0.001953</td>\n",
       "      <td>1.65</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 0]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.001465</td>\n",
       "      <td>1.60</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 0, 1, 0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.001465</td>\n",
       "      <td>1.60</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 1]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability  cost                     solution  count\n",
       "5       0.012695  2.35  [1, 0, 0, 0, 0, 1, 0, 1, 0]     26\n",
       "14      0.008789  2.35  [1, 0, 0, 0, 1, 0, 0, 0, 1]     18\n",
       "33      0.005859  2.20  [0, 1, 0, 1, 0, 0, 0, 0, 1]     12\n",
       "11      0.010254  2.20  [0, 0, 1, 1, 0, 0, 0, 1, 0]     21\n",
       "9       0.010742  2.20  [0, 1, 0, 0, 0, 1, 1, 0, 0]     22\n",
       "13      0.009277  2.20  [0, 0, 1, 0, 1, 0, 1, 0, 0]     19\n",
       "80      0.003906  1.70  [1, 0, 0, 0, 0, 0, 0, 1, 0]      8\n",
       "167     0.001953  1.65  [1, 0, 0, 0, 1, 0, 0, 0, 0]      4\n",
       "226     0.001465  1.60  [0, 0, 0, 1, 0, 0, 0, 1, 0]      3\n",
       "241     0.001465  1.60  [1, 0, 0, 0, 0, 0, 0, 0, 1]      3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from classiq.applications.combinatorial_optimization import (get_optimization_solution_from_pyo, )\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy)\n",
    "\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "\n",
    "print(\"\\n\\033[1m\\033[4mTop 10 Solutions\\033[0m\")\n",
    "optimization_result.sort_values(by=\"cost\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of Cost and Weighted by Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "optimization_result[\"cost\"].plot(\n",
    "    kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n",
    ")\n",
    "plt.ylabel(\"Probability\", fontsize=12)\n",
    "plt.xlabel(\"Cost\", fontsize=12)\n",
    "plt.tick_params(axis=\"both\", labelsize=12)\n",
    "plt.title(\"Histogram of Cost Weighted by Probability\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Network Graph for the Best Solution Found\n",
    "<b>$\\star$ Very important to remember that this is a mximization problem and the classical solver of the QAOA process returns all possible results. We need to filter out the solution with the highest cost which would represent the the highest compatability score. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4m** QAOA SOLUTION **\u001b[0m\n",
      "\u001b[4mHighest Compatibility Score\u001b[0m =  2.3499999999999996\n",
      "        patient1  patient2  patient3\n",
      "donor1         1         0         0\n",
      "donor2         0         0         1\n",
      "donor3         0         1         0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This function plots the solution in a table and a graph\n",
    "\n",
    "\n",
    "def plotting_sol(x_sol, cost):\n",
    "    x_sol_to_mat = np.reshape(np.array(x_sol), [N, M])  # vector to matrix\n",
    "    print(\"\\033[1m\\033[4m** QAOA SOLUTION **\\033[0m\")\n",
    "    print(\"\\033[4mHighest Compatibility Score\\033[0m = \" , cost)\n",
    "\n",
    "    # plotting in a table\n",
    "    df = pd.DataFrame(x_sol_to_mat)\n",
    "    df.columns = patients\n",
    "    df.index = donors\n",
    "    print(df)\n",
    "    \n",
    "\n",
    "    # plotting in a graph\n",
    "    graph_sol = nx.DiGraph()\n",
    "    graph_sol.add_nodes_from(donors+patients)\n",
    "    for n, m in product(range(N), range(M)):\n",
    "        if x_sol_to_mat[n, m] > 0:\n",
    "            graph_sol.add_edges_from(\n",
    "                [(donors[m], patients[n])]   , weight=compatibility_scores[(donors[m], patients[n])]\n",
    "            )\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    left = nx.bipartite.sets(graph_sol, top_nodes=patients)[0]\n",
    "    pos = nx.bipartite_layout(graph_sol, left)\n",
    "\n",
    "    nx.draw_networkx(\n",
    "        graph_sol, pos=pos, nodelist=patients, font_size=22, font_color=\"None\"\n",
    "    )\n",
    "    nx.draw_networkx_nodes(\n",
    "        graph_sol, pos, nodelist=patients, node_color=\"#119DA4\", node_size=500\n",
    "    )\n",
    "    for d in donors:\n",
    "        x, y = pos[d]\n",
    "        plt.text(\n",
    "                x,\n",
    "                y,\n",
    "                s=d,\n",
    "                bbox=dict(facecolor=\"#F43764\", alpha=1),\n",
    "                horizontalalignment=\"center\",\n",
    "                fontsize=12,\n",
    "            )\n",
    " \n",
    "\n",
    "    nx.draw_networkx_edges(graph_sol, pos, width=2)\n",
    "    labels = nx.get_edge_attributes(graph_sol, \"weight\")\n",
    "    nx.draw_networkx_edge_labels(graph_sol, pos, edge_labels=labels, font_size=12, label_pos=.6)\n",
    "    nx.draw_networkx_labels(\n",
    "        graph_sol,\n",
    "        pos,\n",
    "        labels={co: co for co in patients},\n",
    "        font_size=12,\n",
    "        #font_color=\"#F4F9E9\",\n",
    "    )\n",
    "    plt.title(\"Network Graph of the Best Solution\", fontsize=16)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "#best_solution = optimization_result.loc[optimization_result.probability.idxmax()]\n",
    "#plotting_sol(best_solution.solution, best_solution.probability)\n",
    "\n",
    "best_solution = optimization_result.loc[optimization_result.cost.idxmax()]\n",
    "plotting_sol(best_solution.solution, best_solution.cost)"
   ]
  }
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